DESCRIPTION (provided by investigator): The aim of this 4-year project is to develop and test a new personal monitoring device to measure physical, social and cognitive functioning in continuous time and in real life settings. The proposed device, called the LIFEmeter, combines four technologies: accelerometry (motion sensors), digital audio recording (for capturing speech), automatic speech recognition (for fast efficient analysis of speech), and location identification (to explore environmental influences on function). This light-weight, compact, and wearable device will be tested and validated in three phases of data collection. We will also construct the first automatic speech recognition (ASR) system designed to transcribe and analyze the natural speech of older adults. New metrics and methods will be developed to analyze complex time-embedded data on functioning. The proposed system will overcome biases and limitations found in current self-report techniques. Our team combines expertise from the MIT Media Lab (sensors and wearable computing), the JHU Center for Language and Speech Processing (ASR) and the Bloomberg School of Public Health, which is uniquely able to deliver an innovative approach to measuring complex function with potentially broad applicability. The proposed research builds on a previous 5-year cohort study (The Baltimore Memory Study, AG19604) of community-dwelling older adults. Existing data from this study allow us to compare and validate measures of function obtained from our new device against a range of self-reported and clinically-measured outcomes. We will also validate our instrument against the most widely used accelerometer (called ActiGraph). Data gathered using our new device will allow us to study of the impact of the built and social environment on functioning with improved precision. .A key goal will also be to create and disseminate tools that allow other investigators to adopt, refine and test this new approach. RELEVANCE: Accurate and reliable assessment of physical, social and cognitive functioning is the most important measurement challenge in gerontology. Effective health policy for an aging society requires the ability to determine who is able to live independently and who requires care. Current approaches rely on patient self-reports or lab-based tests which are known to be problematic. Improved measurement tools will allow for better testing of the effectiveness of treatments and services, as well as greater accuracy in population health monitoring. The proposed research is responsive to NIH PA-05-090 and to objectives laid out in the NIH roadmap.